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20 | 20 | "cell_type": "markdown", |
21 | 21 | "metadata": {}, |
22 | 22 | "source": [ |
23 | | - "### Task 1: look at the data\n", |
| 23 | + "### Task 1 -- Part (a): Look at the data\n", |
24 | 24 | "In the following code block, we import the ``load_penguins`` function from the ``palmerpenguins`` package.\n", |
25 | 25 | "\n", |
26 | 26 | "- Call this function, which returns a single object, and assign it to the variable ``data``.\n", |
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346 | 346 | "cell_type": "markdown", |
347 | 347 | "metadata": {}, |
348 | 348 | "source": [ |
349 | | - "### Task 2: creating a ``torch.utils.data.Dataset``\n", |
| 349 | + "### Task 2: Creating a ``torch.utils.data.Dataset``\n", |
350 | 350 | "\n", |
351 | 351 | "The penguin data reading and processing can be encapsulated in a PyTorch dataset class.\n", |
352 | 352 | "\n", |
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493 | 493 | "cell_type": "markdown", |
494 | 494 | "metadata": {}, |
495 | 495 | "source": [ |
496 | | - "### Task 3: obtaining training and validation datasets\n", |
| 496 | + "### Task 3: Obtaining training and validation datasets\n", |
497 | 497 | "\n", |
498 | 498 | "- Instantiate the penguin dataloader.\n", |
499 | 499 | " - Make sure you supply the correct column titles for the features and the targets.\n", |
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573 | 573 | "Modify the `PenguinDataset` class above so that the tuples of numbers are converted to PyTorch `torch.Tensor` s and the string targets are converted to indices.\n", |
574 | 574 | "\n", |
575 | 575 | "- Begin by importing relevant PyTorch functions.\n", |
576 | | - "- Complete `__len__()` and `__getitem__()` function above.\n", |
| 576 | + "- Complete the `__getitem__()` function above.\n", |
577 | 577 | "\n", |
578 | 578 | "Then create a training and validation set.\n", |
579 | 579 | "\n", |
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